6 datasets found
  1. f

    Data from: Does wage reflect labor productivity? A comparison between Brazil...

    • scielo.figshare.com
    jpeg
    Updated May 31, 2023
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    ALEXANDRE GORI MAIA; ARTHUR SAKAMOTO (2023). Does wage reflect labor productivity? A comparison between Brazil and the United States [Dataset]. http://doi.org/10.6084/m9.figshare.7367543.v1
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    jpegAvailable download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    SciELO journals
    Authors
    ALEXANDRE GORI MAIA; ARTHUR SAKAMOTO
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Brazil, United States
    Description

    ABSTRACT The study compares the relationship between wages and labor productivity for different categories of workers in Brazil and in the U.S. Analyses highlight to what extent the equilibrium between wages and productivity is related to the degree of economic development. Wages in the U.S. has shown to be more attached to labor productivity, while Brazil has experienced several economic cycles were average earnings grew initially much faster than labor productivity, suddenly falling down in the subsequent years. Analyses also stress how wage differentials, in fact, match productivity differentials for certain occupational groups, while for others they do not.

  2. Poverty-Level Wages in the USA Dataset (1973-2022)

    • kaggle.com
    Updated Nov 7, 2023
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    asaniczka (2023). Poverty-Level Wages in the USA Dataset (1973-2022) [Dataset]. http://doi.org/10.34740/kaggle/ds/3836090
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 7, 2023
    Dataset provided by
    Kaggle
    Authors
    asaniczka
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    United States
    Description

    This dataset provides information on poverty-level wages in the United States from 1973 to 2022.

    It includes data on both annual and hourly poverty-level wages, as well as wage shares for different income brackets.

    The dataset is based on the Economic Policy Institute’s State of Working America Data Library, which offers comprehensive economic data for analyzing trends and patterns in the labor market.

    Intresting Task Ideas:

    1. Analyze the trend of annual poverty-level wages over the years.
    2. Compare the hourly poverty-level wages between men and women.
    3. Investigate the share of workers earning below poverty-level wages based on race.
    4. Explore the distribution of wages across different poverty wage ranges.
    5. Examine the income disparities across different income brackets (0-75%, 75-100%, etc.) for men and women.
    6. Determine the proportion of workers earning above the poverty level (300%+) over time.
    7. Calculate the percentage change in poverty-level wages from one year to another.

    If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

    Checkout my other datasets

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    USA Unemployment Rates by Demographics & Race

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    Photo by Jon Tyson on Unsplash

  3. a

    Goal 8: Promote sustained, inclusive and sustainable economic growth, full...

    • sdg-hub-template-test-local-2030.hub.arcgis.com
    • eswatini-1-sdg.hub.arcgis.com
    • +8more
    Updated May 20, 2022
    + more versions
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    Hawaii Local2030 Hub (2022). Goal 8: Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all - Mobile [Dataset]. https://sdg-hub-template-test-local-2030.hub.arcgis.com/datasets/goal-8-promote-sustained-inclusive-and-sustainable-economic-growth-full-and-productive-employment-and-decent-work-for-all-mobile
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    Dataset updated
    May 20, 2022
    Dataset authored and provided by
    Hawaii Local2030 Hub
    Description

    Goal 8Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allTarget 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countriesIndicator 8.1.1: Annual growth rate of real GDP per capitaNY_GDP_PCAP: Annual growth rate of real GDP per capita (%)Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high-value added and labour-intensive sectorsIndicator 8.2.1: Annual growth rate of real GDP per employed personSL_EMP_PCAP: Annual growth rate of real GDP per employed person (%)Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial servicesIndicator 8.3.1: Proportion of informal employment in total employment, by sector and sexSL_ISV_IFEM: Proportion of informal employment, by sector and sex (ILO harmonized estimates) (%)Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the leadIndicator 8.4.1: Material footprint, material footprint per capita, and material footprint per GDPEN_MAT_FTPRPG: Material footprint per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollar)EN_MAT_FTPRPC: Material footprint per capita, by type of raw material (tonnes)EN_MAT_FTPRTN: Material footprint, by type of raw material (tonnes)Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDPEN_MAT_DOMCMPT: Domestic material consumption, by type of raw material (tonnes)EN_MAT_DOMCMPG: Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollars)EN_MAT_DOMCMPC: Domestic material consumption per capita, by type of raw material (tonnes)Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueIndicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilitiesSL_EMP_EARN: Average hourly earnings of employees by sex and occupation (local currency)Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilitiesSL_TLF_UEM: Unemployment rate, by sex and age (%)SL_TLF_UEMDIS: Unemployment rate, by sex and disability (%)Target 8.6: By 2020, substantially reduce the proportion of youth not in employment, education or trainingIndicator 8.6.1: Proportion of youth (aged 15–24 years) not in education, employment or trainingSL_TLF_NEET: Proportion of youth not in education, employment or training, by sex and age (%)Target 8.7: Take immediate and effective measures to eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its formsIndicator 8.7.1: Proportion and number of children aged 5–17 years engaged in child labour, by sex and ageSL_TLF_CHLDEC: Proportion of children engaged in economic activity and household chores, by sex and age (%)SL_TLF_CHLDEA: Proportion of children engaged in economic activity, by sex and age (%)Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employmentIndicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant statusSL_EMP_FTLINJUR: Fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)SL_EMP_INJUR: Non-fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant statusSL_LBR_NTLCPL: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislationTarget 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and productsIndicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rateST_GDP_ZS: Tourism direct GDP as a proportion of total GDP (%)Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for allIndicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adultsFB_ATM_TOTL: Number of automated teller machines (ATMs) per 100,000 adultsFB_CBK_BRCH: Number of commercial bank branches per 100,000 adultsIndicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service providerFB_BNK_ACCSS: Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older)Target 8.a: Increase Aid for Trade support for developing countries, in particular least developed countries, including through the Enhanced Integrated Framework for Trade-related Technical Assistance to Least Developed CountriesIndicator 8.a.1: Aid for Trade commitments and disbursementsDC_TOF_TRDCMDL: Total official flows (commitments) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBMDL: Total official flows (disbursement) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBML: Total official flows (disbursement) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_TRDCML: Total official flows (commitments) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)Target 8.b: By 2020, develop and operationalize a global strategy for youth employment and implement the Global Jobs Pact of the International Labour OrganizationIndicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategySL_CPA_YEMP: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy

  4. 2025 Green Card Report for Quality Productivity Systems

    • myvisajobs.com
    Updated Jan 16, 2025
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    MyVisaJobs (2025). 2025 Green Card Report for Quality Productivity Systems [Dataset]. https://www.myvisajobs.com/reports/green-card/major/quality--productivity-systems
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    Dataset updated
    Jan 16, 2025
    Dataset provided by
    MyVisaJobs.com
    Authors
    MyVisaJobs
    License

    https://www.myvisajobs.com/terms-of-service/https://www.myvisajobs.com/terms-of-service/

    Variables measured
    Major, Salary, Petitions Filed
    Description

    A dataset that explores Green Card sponsorship trends, salary data, and employer insights for quality productivity systems in the U.S.

  5. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Non...

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Non Labor Pay [Dataset]. https://www.ceicdata.com/en/united-states/productivity-and-costs-index-1992100-seasonally-adjusted-discontinued/productivity--cost-index-sa-mfg-non-durable-unit-non-labor-pay
    Explore at:
    Dataset updated
    Feb 15, 2025
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 1996 - Dec 1, 2007
    Area covered
    United States
    Variables measured
    Job Market Indicators
    Description

    United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Non Labor Pay data was reported at 177.700 1992=100 in 2007. This records an increase from the previous number of 168.400 1992=100 for 2006. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Non Labor Pay data is updated yearly, averaging 59.400 1992=100 from Dec 1949 (Median) to 2007, with 59 observations. The data reached an all-time high of 177.700 1992=100 in 2007 and a record low of 22.600 1992=100 in 1949. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Non Labor Pay data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G068: Productivity and Costs Index: 1992=100: Seasonally Adjusted (Discontinued).

  6. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Nonlabor...

    • ceicdata.com
    Updated Mar 15, 2023
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    CEICdata.com (2023). United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Nonlabor Pay [Dataset]. https://www.ceicdata.com/en/united-states/productivity-and-costs-index-2009100-seasonally-adjusted/productivity--cost-index-sa-mfg-non-durable-unit-nonlabor-pay
    Explore at:
    Dataset updated
    Mar 15, 2023
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2004 - Dec 1, 2015
    Area covered
    United States
    Variables measured
    Job Market Indicators
    Description

    United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Nonlabor Pay data was reported at 105.148 2009=100 in 2016. This records a decrease from the previous number of 109.750 2009=100 for 2015. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Nonlabor Pay data is updated yearly, averaging 67.793 2009=100 from Dec 1987 (Median) to 2016, with 30 observations. The data reached an all-time high of 129.987 2009=100 in 2012 and a record low of 49.484 2009=100 in 1987. United States Productivity & Cost Index: sa: Mfg: Non Durable: Unit Nonlabor Pay data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G067: Productivity and Costs Index: 2009=100: Seasonally Adjusted.

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ALEXANDRE GORI MAIA; ARTHUR SAKAMOTO (2023). Does wage reflect labor productivity? A comparison between Brazil and the United States [Dataset]. http://doi.org/10.6084/m9.figshare.7367543.v1

Data from: Does wage reflect labor productivity? A comparison between Brazil and the United States

Related Article
Explore at:
jpegAvailable download formats
Dataset updated
May 31, 2023
Dataset provided by
SciELO journals
Authors
ALEXANDRE GORI MAIA; ARTHUR SAKAMOTO
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
Brazil, United States
Description

ABSTRACT The study compares the relationship between wages and labor productivity for different categories of workers in Brazil and in the U.S. Analyses highlight to what extent the equilibrium between wages and productivity is related to the degree of economic development. Wages in the U.S. has shown to be more attached to labor productivity, while Brazil has experienced several economic cycles were average earnings grew initially much faster than labor productivity, suddenly falling down in the subsequent years. Analyses also stress how wage differentials, in fact, match productivity differentials for certain occupational groups, while for others they do not.

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